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Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

1
52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Sampling Polytopes with Riemannian HMC: Faster Mixing via the Lewis Weights Barrier COLT 2024 Sampling-Resilient Multi-Object Tracking AAAI 2024 SaProt: Protein Language Modeling with Structure-aware Vocabulary ICLR 2024 Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features ICML 2024 SasWOT: Real-Time Semantic Segmentation Architecture Search WithOut Training AAAI 2024 SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning NeurIPS 2024 Scalable and Effective Implicit Graph Neural Networks on Large Graphs ICLR 2024 Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes NeurIPS 2024 Scalable Counterfactual Distribution Estimation in Multivariate Causal Models CLeaR 2024 Scalable Data Ablation Approximations for Language Models through Modular Training and Merging EMNLP 2024 Scalable DP-SGD: Shuffling vs. Poisson Subsampling NeurIPS 2024 Scalable Efficient Training of Large Language Models with Low-dimensional Projected Attention EMNLP 2024 Scalable Federated Unlearning via Isolated and Coded Sharding IJCAI 2024 Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach EMNLP 2024 Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers AAAI 2024 Scalable High-Dimensional Multivariate Linear Regression for Feature-Distributed Data JMLR 2024 Scalable Kernel Inverse Optimization NeurIPS 2024 Scalable Learning of Item Response Theory Models AISTATS 2024 Scalable Meta-Learning with Gaussian Processes AISTATS 2024 Scalable Multiple Kernel Clustering: Learning Clustering Structure from Expectation ICML 2024 Scalable Neural Network Kernels ICLR 2024 Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planes NeurIPS 2024 Scalable Online Exploration via Coverability ICML 2024 Scalable Optimization in the Modular Norm NeurIPS 2024 Scalable Resampling in Massive Generalized Linear Models via Subsampled Residual Bootstrap JMLR 2024